One of our main activities over the last few years has been the development of a comprehensive model for oscillations of membrane potential and calcium on time scales ranging from seconds to minutes. These lead to corresponding oscillations of insulin secretion. The basic hypothesis of the model is that the faster oscillations (tens of seconds) stem from feedback of calcium onto ion channels, likely calcium-activated potassium (K(Ca)) channels and ATP-dependent potassium (K(ATP)) channels, whereas the slower oscillations (five minutes) stem from oscillations in metabolism. The metabolic oscillations are transduced into electrical oscillations via the K(ATP) channels. The model thus consists of an electrical oscillator (EO) and a metabolic oscillator (MO) and is referred to as the Dual Oscillator Model (DOM). In our model, the MO is a glycolytic oscillator, but many of the features of the system would still hold if the metabolic oscillation arose elsewhere, such as the mitochondria. We have updated a review of the features of the model in Reference # 1. K(ATP) channels are of clinical significance as they are a first-line target of insulin-stimulating drugs, such as the sulfonylureas tolbutamide and glyburide, used in the treatment of Type 2 Diabetes. Severe gain-of-function mutations of K(ATP) are a major cause of neo-natal diabetes mellitus, whereas moderate gain-of-function mutations have been linked in genome-wide association studies (GWAS) to the milder but more common disease, adult-onset type 2 diabetes. Conversely, loss-of-function mutations of K(ATP) are a major cause of familial hyperinsulinism, a hereditary disease found in children in which beta cells are persistently electrically active and secrete insulin in the face of normal or low glucose, causing life-threatening hypoglycemia. A review of the importance of oscillations of insulin secretion for health and disease was published in Reference # 3. The strongest case can be made for optimizing the suppression of hepatic glucose production. New simulations were included supporting the hypothesis that oscillatory insulin results in higher peak response to insulin by target tissues because it allows time for negative feedback in the insulin-signaling cascade to recover between pulses. This would apply to any insulin-sensitive tissue, but the liver sees the largest amplitude oscillations because it receives insulin directly from the pancreas through the portal vein. Other simulations demonstrated that pulsatile calcium oscillations in the beta cells result in higher peak insulin release. During the report period we have demonstrated that the DOM can also describe the oscillations that have been observed in cAMP. cAMP is of high current clinical interest because it amplifies the ability of calcium to promote insulin secretion. It does this mainly by increasing the number of available insulin vesicles for calcium to act on. cAMP in beta cells is mainly controlled by the action of glucagon-like peptide 1 (GLP-1), a hormone secreted by cells in the intestine when glucose rises during digestion of a meal. Two classes of drugs for Type 2 diabetes target this pathway, either by acting as analogues to GLP-1 or by slowing its degradation, which increases GLP-1 concentration. It is thus of interest to understand the mechanisms that regulate cAMP in beta cells. Previous models by others have focused on the regulation of cAMP by calcium, which activates both the enzyme that makes cAMP (adenylyl cyclase) and the enzyme that degrades it (phosphodiesterase). However, data from the Tengholm group in Uppsala, Sweden show that cAMP oscillations can occur also in the absence of calcium oscillations. We hypothesized that metabolic oscillations, which we had previously shown can also occur in the absence of calcium oscillations, influence cAMP production by inhibiting adenylyl cyclase. Adding this feature to the DOM allows it to account for cAMP oscillations both in the presence and absence of calcium oscillations. This work is described in Reference # 2.